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The monitoring and analysis of the spatiotemporal distribution of anthropogenic carbon emissions is an important part of realizing China’s regional “dual carbon” goals; that is, the aim is for carbon emissions to peak in 2030 an to achieve carbon neutrality by 2060, as well as achieving sustainable development of the ecological environment. The column-averaged CO2 dry air mole fraction (XCO2) of greenhouse gas remote sensing satellites has been widely used to monitor anthropogenic carbon emissions. However, selecting a reasonable background region to eliminate the influence of uncertainty factors is still an important challenge to monitor anthropogenic carbon emissions by using XCO2. Aiming at the problems of the imprecise selection of background regions, this study proposes to enhance the anthropogenic carbon emission signal in the XCO2 by using the regional comparison method based on the idea of zoning. First, this study determines the background region based on the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) dataset and potential temperature data. Second, the average value of the XCO2 in the background area was extracted and taken as the XCO2 background. On this basis, the XCO2 anomaly (XCO2ano) was obtained by regional comparison method. Finally, the spatiotemporal variation characteristics and trends of XCO2ano were analyzed, and the correlations between the number of residential areas and fossil fuel emissions were calculated. The results of the satellite observation data experiments over China from 2010 to 2020 show that the XCO2ano and anthropogenic carbon emissions have similar spatial distribution patterns. The XCO2ano in China changed significantly and was in a positive growth trend as a whole. The XCO2ano values have a certain positive correlation with the number of residential areas and observations of fossil fuel emissions. The purpose of this research is to enhance the anthropogenic carbon emission signals in satellite observation XCO2 data by combining ODIAC data and potential temperature data, achieve the remote sensing monitoring and analysis of spatiotemporal changes in anthropogenic carbon emissions over China, and provide technical support for the policies and paths of regional carbon emission reductions and ecological environmental protection.

Qinghai Lake Basin is the largest endorheic basin in the northeastern part of the Qinghai-Tibet Plateau (QTP). The vegetation dynamics are subject to dual pressures from climate change and human activities. Previous studies have neglected the interactions among driving factors, as well as the impact of climate factors on vegetation under the regulatory role of topographic elements. The present study utilises MODIS-EVI data from 2001 to 2022 to estimate Fractional Vegetation cover (FVC) and to reveal the spatiotemporal dynamics of vegetation cover through trend analysis and other methods. Furthermore, it elucidates the effect of topographical factors on vegetation distribution. Finally, geographic detectors and the partial least squares structural equation model (PLS-SEM) were employed to quantify the impact intensity of driving factors (including climate, human activities, topography, and soil) and analyze their interactive effects and influence pathways on vegetation cover. The results suggested that (1) FVC in the Qinghai Lake Basin increased significantly (1.38×10 - ³/a); notably, low-grade FVC areas exhibiting high volatility. (2) The terrain effect displays clear differentiation characteristics. FVC peaks in the elevation range of 3500–3800 m, FVC dispersion increased with slope, and semishady/shady slopes dominated FVC distribution. The vegetation improvement type is concentrated on low-elevation, flat slopes and shady slopes, whereas the vegetation degradation type is distributed on middle- and low-elevation slopes and semipositive slopes. (3) Climatic factors primarily exert a direct positive influence on FVC. As far as climate factors are concerned, the effects of temperature and precipitation on FVC do not act independently, but act together through synergistic effects, with temperature showing a more significant driving effect. Topography primarily affects FVC indirectly by regulating water and heat conditions (temperature and precipitation). Each factor possesses an optimal range (elevation: 3400–4100 m, precipitation: 325–550 mm, temperature: −6 to 0°C). When changes in these driving factors exceed the optimal range, FVC is suppressed. On a temporal scale, climate change and human activities are the dominant factors influencing the FVC in the Qinghai Lake Basin. The positive effects of human factors on FVC have strengthened.

Fractional vegetation coverage (FVC) is an important indicator of ecosystem change. At present, FVC products are mainly concentrated at low and medium spatial resolution and lack high temporal and spatial resolution, which brings certain challenges to the fine monitoring of ecological environments. In this study, we evaluated the accuracy of four remote sensing inversion models for FVC based on high-spatial-resolution Sentinel-2 imagery and unmanned aerial vehicle (UAV) field-measured FVC data in 2019. Then the inversion models were optimized by constructing a multidimensional feature dataset. Finally, the Source Region of the Yellow River (SRYR) FVC product was created using the best inversion model, and the spatial-temporal variation characteristics of the FVC in the region were analyzed. The study’s findings revealed that: (1) The accuracies of the four FVC inversion models were as follows: the Gradient Boosting Decision Tree (GBDT) model (R2 = 0.967, RMSE = 0.045) > Random Forest (RF) model (R2 = 0.962, RMSE = 0.049) > Support Vector Machine (SVM) model (R2 = 0.925, RMSE = 0.072) > Pixel Dichotomy (PD) model (R2 = 0.869, RMSE = 0.097). (2) Constructing a multidimensional feature dataset to optimize the driving data can improve the accuracy of the inversion model. NDVI and elevation are important factors affecting the accuracy of machine learning inversion algorithms, and the visible blue band is the most important feature factor of the GBDT model. (3) The FVC in the SRYR gradually increased from west to east and from north to south. The change trajectories of grassland FVC from 2017 to 2022 were not significant. The areas that tend to improve were mainly distributed in the southeast (1.31%), while the areas that tend to degrade were mainly distributed in the central and northwest (1.89%). This study provides a high-spatial-resolution FVC inversion optimization scheme, which is of great significance for the fine monitoring of alpine grassland ecological environments.

In the context of global climate change and growing food security challenges, this study provides a comprehensive analysis of the yields of three staple crops (wheat, corn and rice) in the Yellow River Basin of China, employing multiple quantitative analysis methods including the Mann–Kendall trend test, center of gravity transfer model and hotspot analysis. Our research integrates yield data covering these three crops from 72 prefecture-level cities across the Yellow River Basin, during 2000 to 2023, to systematically examine the temporal variation, spatial variation and spatial agglomeration characteristics of the yields. The study uses GeoDetector to explore the impacts of natural and socioeconomic factors on changes in crop yields from both single-factor and interactive-factor perspectives. While traditional statistical methods often struggle to simultaneously handle complex causal relationships among multiple factors, particularly in effectively distinguishing between direct and indirect influence paths or accounting for the transmission effects of factors through mediating variables, this study adopts Structural Equation Modeling (SEM) to identify which factors directly affect crop yields and which exert indirect effects through other factors. This approach enables us to elucidate the path relationships and underlying mechanisms governing crop yields, thereby revealing the direct and indirect influences among multiple factors. This study conducted an analysis using Structural Equation Modeling (SEM), classifying the intensity of influence based on the absolute value of the impact factor (with >0.3 defined as “strong”, 0.1–0.3 as “moderate” and <0.1 as “weak”), and distinguishing the nature of influence by the positive or negative value (positive values indicate promotion, negative values indicate inhibition). The results show that among natural factors, temperature has a moderate promoting effect on wheat (0.21) and a moderate inhibiting effect on corn (−0.25); precipitation has a moderate inhibiting effect on wheat (−0.28) and a moderate promoting effect on rice (0.17); DEM has a strong inhibiting effect on wheat (−0.33) and corn (−0.58), and a strong promoting effect on rice (0.38); slope has a moderate inhibiting effect on wheat (−0.15) and a moderate promoting effect on corn (0.15). Among socioeconomic factors, GDP has a weak promoting effect on wheat (0.01) and a moderate inhibiting effect on rice (−0.20), while the impact of population is relatively small. In terms of indirect effects, slope indirectly inhibits wheat (−0.051, weak) and promotes corn (0.149, moderate) through its influence on temperature; DEM indirectly promotes rice (0.236, moderate) through its influence on GDP and precipitation. In terms of interaction effects, the synergy between precipitation and temperature has the highest explanatory power for wheat and rice, while the synergy between DEM and precipitation has the strongest explanatory power for corn. The study further analyzes the mechanisms of direct and indirect interactions among various factors and finds that there are significant temporal and spatial differences in crop yields in the Yellow River Basin, with natural factors playing a leading role and socioeconomic factors showing dynamic regulatory effects. These findings provide valuable insights for sustainable agricultural development and food security policy-making in the region.

Fractional vegetation cover (FVC) is a crucial indicator for measuring the growth of surface vegetation. The changes and predictions of FVC significantly impact biodiversity conservation, ecosystem health and stability, and climate change response and prediction. Southwest China (SWC) is characterized by complex topography, diverse climate types, and rich vegetation types. This study first analyzed the spatiotemporal variation of FVC at various timescales in SWC from 2000 to 2020 using FVC values derived from pixel dichotomy model. Next, we constructed four machine learning models—light gradient boosting machine (LightGBM), support vector regression (SVR), k-nearest neighbor (KNN), and ridge regression (RR)—along with a weighted average heterogeneous ensemble model (WAHEM) to predict growing-season FVC in SWC from 2000 to 2023. Finally, the performance of the different ML models was comprehensively evaluated using tenfold cross-validation and multiple performance metrics. The results indicated that the overall FVC in SWC predominantly increased from 2000 to 2020. Over the 21 years, the FVC spatial distribution in SWC generally showed a high east and low west pattern, with extremely low FVC in the western plateau of Tibet and higher FVC in parts of eastern Sichuan, Chongqing, Guizhou, and Yunnan. The determination coefficient R2 scores from tenfold cross-validation for the four ML models indicated that LightGBM had the strongest predictive ability whereas RR had the weakest. WAHEM and LightGBM models performed the best overall in the training, validation, and test sets, with RR performing the worst. The predicted spatial change trends were consistent with the MODIS-MOD13A3-FVC and FY3D-MERSI-FVC, although the predicted FVC values were slightly higher but closer to the MODIS-MOD13A3-FVC. The feature importance scores from the LightGBM model indicated that digital elevation model (DEM) had the most significant influence on FVC among the six input features. In contrast, soil surface water retention capacity (SSWRC) was the most influential climate factor. The results of this study provided valuable insights and references for monitoring and predicting the vegetation cover in regions with complex topography, diverse climate types, and rich vegetation. Additionally, they offered guidance for selecting remote sensing products for vegetation cover and optimizing different ML models.

In the Xinjiang region, the sustainable management of water resources, energy, and food is crucial for regional development. This study establishes a coupling evaluation index for energy–food–water (EFW) systems from the perspectives of supply, consumption, and efficiency. Using an integrated EFM-CDD-RDD-CCDM approach, an assessment of the coupling and coordination levels of the EFW systems in 14 cities within Xinjiang was conducted for the period of 2004 to 2020. Additionally, the method of obstacle degree identification was utilized to determine the main barriers affecting the EFW systems. Key findings included the following. (1) In terms of individual system coordination indices, the water resource systems exhibited overall higher coordination (ranging from 0.30 to 0.72) with comparatively minor spatial variability, while the energy (from 0.18 to 0.81) and food (from 0.12 to 0.83) systems showed greater temporal and spatial fluctuations. From 2004 to 2020, improvements were observed in the coordination of food and water resource systems, whereas a decline was noted in the coordination of the energy subsystem. (2) Prior to 2011, the coupling of food–water and energy–food systems showed an upward trend, whereas the energy–water coupling decreased annually by 2.62%, further highlighting the tensions between energy development and water resource constraints in Xinjiang. (3) The comprehensive coupling coordination index of the Xinjiang EFW systems ranged between 0.59 and 0.80; between 2004 and 2020, there was an oscillatory increase. From 2004 to 2016, the coupling and coordination degree across the municipalities generally improved, with the regions on the western side and southern slope of the Tianshan Mountains, the Altai Mountains, and the northwestern edge of the Junggar Basin exhibiting the highest levels, followed by the three prefectures in southern Xinjiang. (4) The EFW obstacle degree posed by the food systems in Xinjiang and its divisions showed a decreasing trend from 2004 to 2020, with the energy system identified as the main factor affecting the coupling and coordination degrees of the EFW systems (increasing by 44% to 52%). Therefore, it is imperative to accelerate the energy transition and optimization in the lead energy development and production areas of Xinjiang. This research provides a scientific basis for Xinjiang’s sustainable development strategies and highlights potential directions for the future optimization of resource management.

In arid regions, climatic fluctuations significantly affect vegetation structure and function. Sun-induced chlorophyll fluorescence (SIF) can quantify certain physiological parameters of vegetation but has limitations in characterizing responses to climate change. This study analyzed the spatiotemporal differences in response to climate change across various ecological regions and vegetation types from 2000 to 2020 in Xinjiang. According to China’s ecological zoning, R1 (Altai Mountains-Western Junggar Mountains forest-steppe) and R5 (Pamir-Kunlun Mountains-Altyn Tagh high-altitude desert grasslands) represent two ecological extremes, while R2–R4 span desert and forest-steppe ecosystems. We employed the standardized precipitation evapotranspiration index (SPEI) at different timescales to represent drought intensity and frequency in conjunction with global OCO-2 SIF products (GOSIF) and the normalized difference vegetation index (NDVI) to assess vegetation growth conditions. The results show that (1) between 2000 and 2020, the overall drought severity in Xinjiang exhibited a slight deterioration, particularly in northern regions (R1 and R2), with a gradual transition from short-term to long-term drought conditions. The R4 and R5 ecological regions in southern Xinjiang also displayed a slight deterioration trend; however, R5 remained relatively stable on the SPEI24 timescale. (2) The NDVI and SIF values across Xinjiang exhibited an upward trend. However, in densely vegetated areas (R1–R3), both NDVI and SIF declined, with a more pronounced decrease in SIF observed in natural forests. (3) Vegetation in northern Xinjiang showed a significantly stronger response to climate change than that in southern Xinjiang, with physiological parameters (SIF) being more sensitive than structural parameters (NDVI). The R1, R2, and R3 ecological regions were primarily influenced by long-term climate change, whereas the R4 and R5 regions were more affected by short-term climate change. Natural grasslands showed a significantly stronger response than forests, particularly in areas with lower vegetation cover that are more structurally impacted. This study provides an important scientific basis for ecological management and climate adaptation in Xinjiang, emphasizing the need for differentiated strategies across ecological regions to support sustainable development.

Changes in grassland fractional vegetation coverage (FVC) are important indicators of global climate change. Due to the unique characteristics of the Tibetan Plateau ecosystem, variations in grassland coverage are crucial to its ecological stability. This study utilizes the Google Earth Engine (GEE) platform to retrieve long-term MODIS data and analyzes the spatiotemporal distribution of grassland FVC across the Qinghai–Tibet Plateau (QTP) over 24 years (2000–2023). The grassland growth index (GI) is used to evaluate the annual grassland growth at the pixel level. GI is an important indicator for measuring grassland growth status, which can effectively measure the changes in grassland growth in each year relative to the base year. FVC trends are monitored using Sen-Mann-Kendall slope estimation, the coefficient of variation, and the Hurst exponent. Geographic detectors and partial correlation analysis are then applied to explore the contribution rates of key driving factors to FVC. The results show: (1) From 2000 to 2023, FVC exhibited an overall upward trend, with an annual growth rate of 0.0881%. The distribution of FVC on the QTP follows a pattern of higher values in the east and lower values in the west; (2) Over the past 24 years, 54.05% of the total grassland area has shown a significant increase, 23.88% has remained stable, and only a small portion has shown a significant decrease. The overall trend is expected to continue with minimal variability, covering 82.36% of the total grassland area. The overall grassland GI suggests a balanced state of growth; (3) precipitation (Pre) and soil moisture (SM) are the main single factors affecting FVC changes in grasslands on the Tibetan Plateau (q = 0.59 and 0.46). In the interaction detection, in addition to the highest interaction between Pre and other factors, the interaction between SM and other factors also showed a significant impact on the changes in FVC of the QTP grassland; partial correlation analysis of hydrothermal factors and FVC of the QTP grassland. It shows that precipitation has a stronger correlation with QTP grassland FVC changes than temperature. This study has enhanced our understanding of grassland vegetation change and its driving factors on the QTP and quantitatively described the relationship between vegetation change and driving factors, which is of great significance for maintaining the sustainable development of grassland ecosystems.

The variations in snow cover on the Tibetan Plateau play a pivotal role in comprehending climate change patterns and governing hydrological processes within the region. This study leverages daily snow cover data and the NASA Digital Elevation Model (DEM) from 2003 to 2020 to analyze spatiotemporal snow cover days and assess their responsiveness to climatic shifts by integrating meteorological data. The results reveal significant spatial heterogeneity in snow cover across the Plateau, with a slight decreasing trend in annual average snow cover duration. Snow cover is predominantly observed during the spring and winter seasons, constituting approximately 32% of the total snow cover days annually. The onset and cessation of snow cover occur within a range of 120–220 days. Additionally, an increasing trend in snow cover duration below 5000 m altitude was observed, in addition to a decreasing trend above 5000 m altitude. Sub-basin analysis delineates the Tarim River Basin as exhibiting the lengthiest average annual snow cover duration of 83 days, while the Yellow River Basin records the shortest duration of 31 days. The decreasing trend in snow cover duration closely aligns with climate warming trends, characterized by a warming rate of 0.17 ± 0.54 °C per decade, coupled with a concurrent increase in precipitation at a rate of 3.09 ± 3.81 mm per year. Temperature exerts a more pronounced influence on annual snow cover duration variation compared to precipitation, as evidenced by a strong negative correlation (CC = −0.67). This study significantly augments the comprehension of hydrological cycle dynamics on the Tibetan Plateau, furnishing essential insights for informed decision-making in water resource management and ecological conservation efforts.

The rural residential land (RRL) in natural reserves has been deeply transformed due to the disturbance constrained by ecological protection policies. Exploring the distribution characteristics and driving factors of RRL in natural reserves and non-natural reserves will help to promote the governance of land space and alleviate the contradiction of land use. Therefore, taking 122 county-level administrative regions in Hunan Province as an example, this article analyzes and compares the spatiotemporal distribution characteristics of RRL in natural reserves and non-natural reserves by using land use change dynamics, nuclear density analysis, the transfer matrix model, and the ordinary least-squares model and explores how the establishment of natural reserves affects the RRL area change. The results show that (1) the overall RRL area in Hunan changed from 171,162.27 hm 2 in 2000 to 169,914.6 hm 2 in 2020, with a total reduction of 1,247.67 hm 2 and a decrease of 0.73%, and the distribution of the RRL area change presented a hot trend in the northeast and a cold trend in the southwest. (2) The occupation of urban construction land is the main reason for the reduction in RRL area, and the transformation of cultivated land and forestland into RRL is the main source of the increase in RRL area. (3) During 2000–2020, the overall RRL in natural reserves increased by 1,538.37 hm 2 , with an increase of 0.11%, while the overall RRL in non-natural reserves decreased by 2,786.04 hm 2 , with a decrease of 0.14%. (4) The establishment of natural reserves has a significant negative correlation with the area of RRL in 2000, 2010, and 2020, indicating that the establishment of natural reserves can limit the growth speed of the RRL area to a certain extent, but is affected by factors such as economic development and rural population growth; it cannot directly promote the overall reduction of RRL area. The results of this study can provide a reference for decision-making related to the spatial structure optimization of natural reserves and non-natural protected RRL and the coordinated development of urban and rural areas.